September 5-6, 2018, RISE AI is presenting work at the Swedish symposium on deep learning. We have a poster about our work on blood glucose prediction with confidence estimation, and an oral presentation about character-based recurrent neural networks for morphological transformations. Come and talk to us!
Swedish symposium on deep learning
Olof Mogren
Given a demo relation (a pair of word forms) and a query word, we devise a character-based recurrent neural network architecture using three separate encoders and a decoder, trained to predict the missing second form of the query word. Our results show that the exact form can be predicted for English with an accuracy of 94.7%. For Swedish, which has a more complex morphology with more inflectional patterns for nouns and verbs, the accuracy is 89.3%.
To appear at Subword & Character Level Models in NLP (SCLeM) workshop at EMNLP 2017 in Copenhagen, Denmark, September 7.
Olof Mogren, Richard Johansson
Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.
Constructive Machine Learning Workshop (CML) at NIPS 2016 in Barcelona, Spain, December 10.
Olof Mogren
EMNLP 2017
2017-09-13
The EMNLP conference took place in Copenhagen in September 2017. In this blog post I share some observations that I made during the conference. These included subword-level models, multilingual NLP, language grounding, and inspiration from children.
ACL 2016
2016-08-22
August 7-12, the 54th conference of the Association of Computational Linguistics (ACL) took place at the Humboldt University in Berlin. This blog post contains a write-up of some of my favourite presentations during the conference.
On March 23rd, 2018 at 10:00, I successfully defended my doctorate thesis titled
“Representation learning for natural language”.
Opponent was
Professor Doktor Hinrich Schütze.
On November 20th, 2015 at 10:00, I successfully defended my licentiate thesis titled
“Multi-document summarization and semantic relatedness”.
Discussion leader was
Tapani Raiko from Aalto University.
The following students recently wrote their master's theses under my supervision.
Are you looking to write a master thesis related to machine learning?:
Several different projects related to machine learning awaits skilled students.
Master students wanted for a number of different master thesis projects.
2018-11-06:
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(Swedish symposium on deep learning)
September 5-6, 2018, RISE AI is presenting work at the Swedish symposium on deep learning. We have a poster about our work on blood glucose prediction with confidence estimation, and an oral presentation about character-based recurrent neural networks for morphological transformations. Come and talk to us!
(Vetenskapsfestivalen)
During the science festival in Gothenburg, we had a session discussing artificial intelligence. The theme for the whole festival was “trust”, so we naturally named our session “Can we trust AI”. I gave an introduction, and shared my view of some of the recent progress that has been made in AI and machine learning, and then we had four other speakers giving their views of current state of the art. Finally, I chaired a discussion session that was much appreciated with the audience. The room was filled, and many people came up to us afterwards and kept the discussion going. The other speakers were Annika Larsson from Autoliv, Ola Gustavsson from Dagens Nyheter, and Hans Salomonsson from Data Intelligence Sweden AB.
(Chalmers Machine Learning Seminars)
Before the representation learning revolution, hand-crafted features were a prerequisite for a successful application of most machine learning algorithms. Just like learned features have been massively successful in many applications, some recent work has shown that you can also automate the learning algorithms themselves. In this talk, I'll cover some of the related ideas presented at this year's NIPS conference.
(FFR135, Artificial Neural Networks)
A motivational talk about deep artificial neural networks, given to the students in FFR135 (Artificial neural networks). I gave motivations for using deep architechtures, and to learn hierarchical representations for data.
March 23, 2018, I defended my PhD thesis, Representation learning for natural language (click for more info).
During 2016-2017, I was the organizer of Chalmers machine learning seminars.
In 2016, I taught a PhD course in Deep Learning, together with Mikael Kågebäck and Fredrik Johansson. I have also taught Algorithms for Machine Learning and Inference, AI (specifically the parts about probabilistic methods, including probabilistic graphical models), Object Oriented Programming, Data Structures, and Algorithms (basic course, and advanced course).
